Advanced Machine Learning Operations
In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Declarative Automation Bundles (DABs), Lakeflow Jobs, and Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.
Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.
The content was developed for participants with these skills/knowledge/abilities:
• Access to a Databricks workspace with administrator permissions and familiarity with basic Databricks operations (create clusters, run notebooks, basic notebook operations)
• Intermediate experience with Git version control, including repository management and Personal Access Token (PAT) configuration for GitHub integration
• Basic knowledge of CI/CD workflows, pipeline configurations, and DevOps concepts for automated deployment processes
• Intermediate programming experience with Python, MLflow for model tracking and management, and Unity Catalog for data governance
• Familiarity with machine learning model development lifecycle, including feature engineering, model training, validation, and deployment concepts
• Experience with command line interfaces, particularly Databricks CLI installation, configuration, and authentication using personal access tokens
• Understanding of model deployment strategies, including A/B testing, traffic distribution, and real-time inference concepts
• Basic knowledge of Lakeflow Jobs for job creation, task dependencies, and workflow orchestration
Outline
1. Overview of Machine Learning Operations
• Review of MLOps
• Streamlining Development to Deployment
2. Streamlining MLOps with Databricks
• Streamlining MLOps
• Streamlining MLOps with Databricks
• Demo: Building a CI/CD Pipeline with Databricks CLI
• Lab: Building a CI/CD Pipeline with Databricks CLI
3. Model Rollout Strategies with Databricks
• Automate Comprehensive Testing
• Demo: Common Testing Strategies
• Demo: Integration Tests with Lakeflow Jobs
• Model Rollout Strategies with Databricks
• Demo: Model Rollout Strategies with Databricks AI Model Serving
• Lab: Rollout Strategies with Lakeflow Jobs
4. Data Profiling
• Data Profiling
• Demo: Data Profiling Model Quality
• Lab: Monitoring Drift with Data Profiling
5. Build ML Assets as Code
• Build ML Assets as Code
• Demo: Working with Declarative Automation Bundles
Upcoming Public Classes
Date | Time | Your Local Time | Language | Price |
|---|---|---|---|---|
Nov 18 | 01 PM - 05 PM (Australia/Sydney) | - | English | $750.00 |
Nov 18 | 09 AM - 01 PM (Europe/Paris) | - | English | $750.00 |
Nov 18 | 09 AM - 01 PM (America/Los_Angeles) | - | English | $750.00 |
Dec 09 | 09 AM - 01 PM (Asia/Kolkata) | - | English | $750.00 |
Dec 09 | 01 PM - 05 PM (Europe/Paris) | - | English | $750.00 |
Dec 09 | 09 AM - 01 PM (America/New_York) | - | English | $750.00 |
Jan 20 | 09 AM - 01 PM (Asia/Singapore) | - | English | $750.00 |
Jan 20 | 09 AM - 01 PM (Europe/Paris) | - | English | $750.00 |
Jan 20 | 01 PM - 05 PM (America/New_York) | - | English | $750.00 |
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.
Private Class Request
If your company is interested in private training, please submit a request.
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